Constructing a novel Chinese readability classification model using principal component analysis and genetic programming

Yi Shian Lee; Hou Chiang Tseng; Ju Ling Chen; Chun Yi Peng; Tao Hsing Chang; Yao Ting Sung

doi:10.1109/ICALT.2012.134

Constructing a novel Chinese readability classification model using principal component analysis and genetic programming

Yi Shian Lee^*, Hou Chiang Tseng, Ju Ling Chen, Chun Yi Peng, Tao Hsing Chang, Yao Ting Sung

^*此作品的通信作者

研究成果: 書貢獻/報告類型 › 會議論文篇章

4 引文斯高帕斯（Scopus）

摘要

The studies of readability aim to measure the level of text difficulty. Although traditional formulae such as the Flesch-Kincaid formula can properly predict text readability, they are only effective for English text. Other formulae with very few features may result in inaccurate text classification. The study takes into account multiple linguistic features, and attempts to increase the level of accuracy in text classification by adopting a new model which integrates Principal Component Analysis (PCA) with Genetic Programming (GP). Empirical data are utilized to demonstrate the performance of the proposed model.

原文	英語
主出版物標題	Proceedings of the 12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012
頁面	164-166
頁數	3
DOIs	https://doi.org/10.1109/ICALT.2012.134
出版狀態	已發佈 - 2012
事件	12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012 - Rome, 意大利持續時間: 2012 7月 4 → 2012 7月 6

出版系列

名字	Proceedings of the 12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012

其他

其他	12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012
國家/地區	意大利
城市	Rome
期間	2012/07/04 → 2012/07/06

ASJC Scopus subject areas

電腦網路與通信
教育

存取文件

10.1109/ICALT.2012.134

其它文件與鏈接

引用此

Lee, Y. S., Tseng, H. C., Chen, J. L., Peng, C. Y., Chang, T. H., & Sung, Y. T. (2012). Constructing a novel Chinese readability classification model using principal component analysis and genetic programming. 於 Proceedings of the 12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012 (頁 164-166). 文章 6268065 (Proceedings of the 12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012). https://doi.org/10.1109/ICALT.2012.134

Constructing a novel Chinese readability classification model using principal component analysis and genetic programming. / Lee, Yi Shian; Tseng, Hou Chiang; Chen, Ju Ling 等.
Proceedings of the 12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012. 2012. p. 164-166 6268065 (Proceedings of the 12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012).

研究成果: 書貢獻/報告類型 › 會議論文篇章

Lee, YS, Tseng, HC, Chen, JL, Peng, CY, Chang, TH & Sung, YT 2012, Constructing a novel Chinese readability classification model using principal component analysis and genetic programming. 於 Proceedings of the 12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012., 6268065, Proceedings of the 12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012, 頁 164-166, 12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012, Rome, 意大利, 2012/07/04. https://doi.org/10.1109/ICALT.2012.134

Lee YS, Tseng HC, Chen JL, Peng CY, Chang TH, Sung YT. Constructing a novel Chinese readability classification model using principal component analysis and genetic programming. 於 Proceedings of the 12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012. 2012. p. 164-166. 6268065. (Proceedings of the 12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012). doi: 10.1109/ICALT.2012.134

Lee, Yi Shian ; Tseng, Hou Chiang ; Chen, Ju Ling 等. / Constructing a novel Chinese readability classification model using principal component analysis and genetic programming. Proceedings of the 12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012. 2012. 頁 164-166 (Proceedings of the 12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012).

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abstract = "The studies of readability aim to measure the level of text difficulty. Although traditional formulae such as the Flesch-Kincaid formula can properly predict text readability, they are only effective for English text. Other formulae with very few features may result in inaccurate text classification. The study takes into account multiple linguistic features, and attempts to increase the level of accuracy in text classification by adopting a new model which integrates Principal Component Analysis (PCA) with Genetic Programming (GP). Empirical data are utilized to demonstrate the performance of the proposed model.",

keywords = "Genetic programming, Principal component analysis, Readability, Text analysis component",

author = "Lee, {Yi Shian} and Tseng, {Hou Chiang} and Chen, {Ju Ling} and Peng, {Chun Yi} and Chang, {Tao Hsing} and Sung, {Yao Ting}",

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AB - The studies of readability aim to measure the level of text difficulty. Although traditional formulae such as the Flesch-Kincaid formula can properly predict text readability, they are only effective for English text. Other formulae with very few features may result in inaccurate text classification. The study takes into account multiple linguistic features, and attempts to increase the level of accuracy in text classification by adopting a new model which integrates Principal Component Analysis (PCA) with Genetic Programming (GP). Empirical data are utilized to demonstrate the performance of the proposed model.

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Constructing a novel Chinese readability classification model using principal component analysis and genetic programming

摘要

出版系列

其他

ASJC Scopus subject areas

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